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The small amount of training data for many state-of-the-art deeplearning-based Face Recognition (FR) systems causes a marked deterioration intheir performance. Although a considerable amount of research has addressedthis issue by inventing new data augmentation techniques, using either inputspace transformations or Generative Adversarial Networks (GAN) for featurespace augmentations, these techniques have yet to satisfy expectations. In thispaper, we propose an approach named the Face Representation Augmentation (FRA)for augmenting face datasets. To the best of our knowledge, FRA is the firstmethod that shifts its focus towards manipulating the face embeddings generatedby any face representation learning algorithm to create new embeddingsrepresenting the same identity and facial emotion but with an altered posture.Extensive experiments conducted in this study convince of the efficacy of ourmethodology and its power to provide noiseless, completely new facialrepresentations to improve the training procedure of any FR algorithm.Therefore, FRA can help the recent state-of-the-art FR methods by providingmore data for training FR systems. The proposed method, using experimentsconducted on the Karolinska Directed Emotional Faces (KDEF) dataset, improvesthe identity classification accuracies by 9.52 %, 10.04 %, and 16.60 %, incomparison with the base models of MagFace, ArcFace, and CosFace, respectively.

Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction